Ashton Kemerling

Well written web services must have clear and easy to follow logs. Without good logs a programmer will be unable to diagnose or reproduce problems that might arise. And while many systems will keep logs in their reverse proxy like Nginx, a well written system will also have application/domain specific logs written within the application itself.

A common goal of application logs is to tie a given log line to a specific request. Because modern servers are multi-threaded, it is possible for multiple requests to be serviced at the same time, resulting in log messages from different requests being intermingled in the log output.

According to all the documentation I have found, the standard way to resolve this is to use the Mapped Diagnostic Context, or MDC. The MDC wraps a thread local hashmap, whose values can be used in your log patterns. On paper you could generate a UUID per request, insert it into the MDC and presto you have a UUID per request in each log line! Since threads are created per request, the MDC will be empty each time.

Except, that’s not really how Java programming works these days. Threads are expensive, and so for the sake of efficiency modern Java aims to minimize the number of threads created. This means using non-blocking IO (NIO), or reusing threads via Thread Pools where possible, which avoids creating new threads. As such there are quite a few number of APIs in Java 8 and some modern web servers that make it trivial to make your code non-blocking and use a common thread pool.

So, what happens to our MDC when we reuse threads? Well, we end up with the wrong MDC! Here’s some code for Java 8 that shows the problem:

This code is using the new CompletableFuture class from Java 8, which is roughly analogous to Javascript promises. What we do is set the correlation-id, then execute a sequence of asynchronous actions and log the output. If you run this code enough times, you will eventually get the output below:

Pay attention to the last line, which should have a correlation-id of correlation-id-two. Instead we have an earlier correlation-id because we happened to reuse a thread from earlier. This is an unmitigated disaster, as now you don’t have missing logs, you have misleading logs. In a less-simple scenario it would be absolutely impossible to tell whether or not that correlation-id was correct, and you might end up diagnosing bugs that don’t exist within your system because of this.

Java 8 and CompletableFuture make this much more plausible by making it very easy to use the ForkJoinPool. CompletableFuture provides a number of methods to create a new CompletableFuture, or to take one CompletableFuture and transform it into another. Most of these methods have at least two signatures: x and xAsync. The former will do all the work on the current thread, the latter will automatically use the ForkJoinPool to execute asynchronously. Optionally you can provide your own Executor, which might be backed by your own managed thread pool.

From a performance standpoint, this is amazing. I can separate the business logic from the execution strategies completely. With very minor code changes, sometimes only a method name, I can turn synchronous code to asynchronous or back again. But from a logging stand point this is very fragile. Correctly using the MDC depends on staying on the current thread, and Java 8 makes it very easy to hop threads by design. Worse, the above incorrectness is subtle, so your logs could be left incorrect for years without any immediate impact.

Recommendations

Don’t use the MDC, it’s too unsafe. Every time you change threads, you will either get an empty MDC (best case scenario), or MDC values from an older request (worst case scenario). At every single point where you might hop threads, it will be necessary to copy the current MDC settings to a local variable, close over it in your next lambda, and set the new thread’s MDC to the correct value before logging on the new thread. If any new asynchronous step is introduced without doing the above work, the correct values are lost for the rest of the request resulting in malformed or incorrect logs. Considering that adding “Async” to the end of a method name is enough to hop threads, this is a lot of work.

The easiest solution is to forgo logging request specific identifiers. Remove any reference to the MDC from your code and log configuration, and move on. If your request volume is relatively low, this might be a perfectly reasonable solution for you.

If you must track every single action down to a single request, you will need to pass around an object for each request which will contain any request specific information you want to log. You can setup logging interfaces that require the Request object to ensure log uniformity, which means that failing to pass this object will be a compilation error. As a side benefit, these custom logging interfaces would be extremely useful in enforcing consistent log messages, and a good place to inject statistics gathering.

Addendum

The MDC docs are incorrect, with sections of the documentation explicitly referencing out of date information. I draw your attention to the section titled “MDC and Managed Threads” which says the following:

A copy of the mapped diagnostic context can not always be inherited by worker threads from the initiating thread. This is the case when java.util.concurrent.Executors is used for thread management. For instance, newCachedThreadPool method creates a ThreadPoolExecutor and like other thread pooling code, it has intricate thread creation logic.

As it turns out, a copy of the mapped diagnostic context will never be inherited by a worker thread. This change guarantees that each thread gets a new, empty MDC. This documentation is correct for older version of logback, but it is not correct anymore. I’m sure the quote above was accidentally left in place, as the correct behavior is mentioned at the top of the document. It’s just a good reminder to read documentation carefully, and understand that humans can make mistakes in the docs too.

Over the past year my team has been doing something shocking to a lot of
engineers: we’re favoring pure Java over Clojure. We aren’t rewriting all our
Clojure code, but we definitely prefer Java for green field projects.

This post is not going to be a compare and contrast between the two, nor am I
going to bash Clojure. Language compare and contrast posts always descend into
flame wars, and it’s very easy to confuse the result of hard lessons learned
with the benefits of a new language.

Instead I’d like to highlight a very strange aspect of our new Java development,
and I hope that you’re sitting down for this. Except tests, I have fewer than a
dozen if statements currently committed in our Java codebase.

It would be easy to assume that we’re just using Java’s method dispatch to
replace if statements; rather than inspecting data and calling if/else on it,
you can use interfaces and count on the implementation to provide the difference
in behavior. But such an explanation is insufficient: objects don’t magically
construct themselves from unstructured data, and Clojure is not without its own
dynamic dispatch facilities.

Ultimately the real explanation for this strange code design lies in my
colleague’s extremely exceptional Lambda
library. It contains a lot of things that a Haskell/Scala developer would
recognize such as Either types, function utilities, coproducts, etc. etc.

In particular I’d like to draw your eye to the Either type, which has replaced
the vast majority of our explicit if calls. Either is the logical
successor to the Java
8 Optional
type. Optional represents the presence of a value of type T with
Optional::of, or it’s absence with Optional::empty. Either on the
other hand is parameterized to two values, L and R, and represents the presence
of either a value of type L, or a value of type R.

Why is this a logical extension of Optional? Because while Optional is
used to represent a result that may have no value (replacing a null), Either is
used to represent a result that might have been a failure, replacing a thrown
exception, with convention being that left side values represent failure and
right side values representing success.

So, what does that buy us? Well, consider what Optional gets us in the following code snippets.

Optional gives us the ability to say “if a value exists, apply this function to
it” repeatedly. It also gives us the ability to chain successive calls that
return Optionals together with the flatMap function, eliminating the need
for unsightly and error prone manual null checks. It’s also safe for map
and flatMap to go from Optional<A> to Optional<B>, which might
eliminate the need for intermediate variables in your code.

Either give us much of the same, but with the ability to represent why the
computation failed with left values, along with the ability to chain together
functions working on an Either type. All of the greatest hits of functional
programming are provided for Either, including map, flatMap, and filter.

As a concrete example, imagine a hypothetical JSON parsing library. Parsing is
tricky business, you’re all but guaranteed that a failure will happen at
runtime. So how do you handle it? Previously you had 4 choices.

return null

return Optional<ParsedType>

checked exception

unchecked exception

Null is obviously bad, and unchecked exceptions are also risky. Checked
exceptions guarantee that someone will deal with the issue, but they are
extremely annoying, and might result in disparate and different exception
handlers all over the place. Optional is nice, it’s safer than null and marks in
the type signature that failure is an option, but it’s a bit lacking on
explaining why a failure occurred.

What if instead this library returned Either<Exception, JsonNode>, or even
Either<Set<String>, JsonNode>? The potential for failure is in the type
signature again, we don’t need a try/catch, but if we want the unwrapped
JsonNode we have to deal with the potential for a left value. And any functions
we have that operate only on JsonNode can be passed in using the map
function, making chaining a breeze.

Better still we can write other functions that might fail in this form, such as
JSON validation, so that we can chain them together using flatMap. If the
json parsing has failed, flatMap does nothing (it only works on right values),
replacing the need for successive null checks, nested try/catch blocks, or
complicated state checking during exception handling to return the correct
value.

As a result of all this, you can easily imagine a JSON API endpoint looking
something like this:

All the potential failure cases are covered by returning an Either rather than
throwing an exception. The very last step is match, which takes two
functions to unify a potential left or right value to the exact same type, which
in this case is HttpResponse.

What does this get me?

Well, first off I think it’s beautiful. I know that’s a subjective call, but the
data flowing neatly from top to bottom without huge nesting if cases and early
return values is very aesthetically pleasing to me.

More functionally it’s easier to refactor with the help of the compiler. If I
want to add different return status codes to match different scenarios, the
compiler helps me out a lot more than if I’m adding an extra return case. If I
convert the left side to a HttpResponse early, the compiler will helpfully
remind me that the later flatMap calls cannot change Either<HttpResponse,
JsonNode> to Either<Exception, BusinessObject>. Such changes are easily
fixed once the compiler has pointed it out, but extremely hard to find on your
own.

But most fundamentally is that we’ve encoded our code’s states in the type
system, not variable states. The potential for JSON parsing to fail is encoded
in its type, not in the potential for a variable to null, or false, or for an
exception to have been thrown. You’re leaning on the compiler to tell you if
you’ve handled the failure cases properly, as the code won’t compile otherwise.
Now instead of testing for runtime exceptions you only test to make sure that
your business logic is correct.

If you’ve ever been interested in what the Haskell or Scala developers have been
talking about with functional type safety, I’d highly recommend taking a look at
Lambda to get a taste of it in Java.

Edit: TL;DR: This is about how bugs in Clojure are handled by the Clojure Team, not just complaints about specific bugs I’ve seen.

First off, this is not a “I’m quiting in disgust” post. Those are childish and a waste of everyone’s time. But this is a post of frustration as I watch something I really like being slowly allowed to get worse.

First off, some history. My first job out of College was in Common Lisp, and I love/hated it. The power it brought and the pain it brought were both one and the same. No modern libraries, no modern build tools (this was before QuickLisp). One on hand, I loved working with paredit and Emacs, being able to quickly fly about my code and manipulate it in blocks rather than line by line. On the other, I couldn’t help but be envious of those who could actually ask for help from a functioning open source community.

A few years of Python, Ruby, and Javascript later, I found Clojure. And I thought I’d found the solution to literally all of the things. Paredit works again? Check. A thriving open source community? You got it. Deploy as a Jar rather than CL’s hilarious “dump the state of a running program and call it good” setup? Fuck. Yes.

And beyond the superficial things, there was a lot to love, especially coming from a more recent brush with Ruby on Rails. Clojure makes it very easy to make things referentially transparent, and it tends to favor explicit calling semantics over convention (or more derisively, “magic”). This means that a Clojure code base will require more plumbing code, but that also means it’s possible to navigate to the code that does routing and understand how it works, no more having to search through your framework’s codebase just because they do dynamic method creation and method_missing magic.

As far as I was concerned, the editor was the only weak point for Clojure. Back when I got into Clojure Cursive was still brand new, Emacs really was the only editor that was worth using and even it had some stability and usability issues. But I assumed that continued interest would stabilize Emacs, bring Vim up to speed, and improve Cursive to the point where it would be competitive with Emacs/Vim.

But all was not well, and if I’d paid attention I might have noticed a few places where the core Clojure’s teams priorities didn’t seem to make much sense to me. And now that I work in Clojure professionally, I really cannot ignore them or remain silent about them any more.

The core Clojure team prefers green field development over improvements and bug fixes to existing code to a degree that deeply worries me. I no longer trust that any issues I find stand a chance of getting fixed, as all the bugs we’ve posted are either in limbo, or flat out rejected. Multiple members of my team have given up on posting new bugs because they have no faith that it’ll help anyone.

These are pretty heavy and vague accusations, so I’m gonna break this down a bit to make it clearer and easier to digest.

Ignorance or Apathy of Underlying Principles

Programming isn’t math per-se, especially in a language that’s not explicitly based on Category or Type theory. That said a lot of the things that we do are backed or defined by mathematics, and to ignore that is to guarantee bugs. This is most clear in clojure.set which contains functions that are supposed to mirror the definitions created by Set Theory like union, difference, intersection, etc.

And the namespace is completely riddled with bugs. union returns duplicates if some of the inputs are lists instead of sets depending on their length. intersection will either return nonsense values or throw a ClassCastException if you provide it anything other than sets, again dependent on data.

On their own, this is no big deal. Bugs happen, there’s really no point in berating people just because they made a mistake. Instead the bug gets fixed as best and as soon as reality allows and we all move on. In fact, for the above bugs there are two possible fixes: raise an IllegalArgumentException if anything other than sets are provided, or coerce lists and vectors to sets before continuing. Both of these approaches are valid due to the fact that this is a dynamic language that defaults to immutable collection semantics; which one you pick is then a matter of how you want to affect your downstream users.

Oh wait, some of these bugs were filed in 2009, 7 fucking years ago! Here comes the berating. These functions are tiny, a simple implementation of union is one line. And while they’re heavily used, they’re simple in usage and signature; no need to change a lot of call sites to fix this bug. There are only two reasons to explain why these bugs have not been fix; they either do not understand that this is an issue, or they do not care.

Actually, their comments on the issues lets us know that they do not understand that this is an issue. Rich Hickey said in 2009 “the fact that these functions happen to work when the second argument is not a set is an implementation artifact and not a promise of the interface”. How you define getting the wrong type with nonsense values counts as “working” is beyond me. Is it just because it doesn’t throw an Exception? Anyone here prefer bad data instead of exceptions when dealing with functions like this? I doubt it.

Inconsistency Between Best Practices and Clojure Implementation

Clojure includes a pretty powerful concept called protocols. Basically a protocol is an interface that can be added to classes after the fact, and lets you dispatch to different behavior silently at run time.

This is pretty neat, it lets you abstract over multiple data types and include Java classes in the fun. For example ISeq provides all the methods needed to iterate over a collection and it works with all the Clojure and Java data types. So you can use Clojure’s map function over its own data types and Java’s because it depends on the seq interface.

As you can imagine, this is the recommended way to work with things. Rather than having to do cond or if logic on various classes, define and use an appropriate protocol and you’re good to go!

It sounds like a good theory doesn’t it? But Clojure itself doesn’t actually do this. Clojure.core contains 89 calls to instance? in order to check runtime type, instead of the helper methods to check for protocol implementation.

Here is a bug found by my colleague that highlights this issue. List and Vector are both seqs, but into for a map only accepts vectors, lists causes a ClassCastException. This is kind of nuts because an IllegalArgumentException makes more sense, and there’s no practical reason to differentiate between a list of two elements and a vector of two elements. Actually, Clojure considers [1 2] and (list 1 2) to be equal, so this really makes no sense

Even more obnoxious, it was closed as wontfix. Apparently a single sentence in the docs is good enough for the Clojure team, as well as a paper-thin argument about performance on a 2 element list. So not only is this just broken in a barely documented and very surprising way, Clojure itself ends up programmed in a way that isn’t recommended by the Clojure docs.

This has spread to other projects. Om has a bug where lists aren’t acceptable in its data structures, only maps sets and vectors. To say that I was treated pretty shabbily by David Nolen on this issue almost goes without saying. Naturally the intro docs barely call this out, and the docs dedicated to the troubled component does not mention this at all. To be fair, the troubleshooting guide explains this, but in my opinion that’s probably a clue that the bug is common enough that you should find a fix for it.

Show Stopping Bugs Remain Untouched

There are a shocking number of big, bad bugs hiding in the Clojure Jira, some really old

This one about fixing with-open for Clojure defined stuff provided a patch 5 years ago.

Duelling ClassLoaders first discovered last year. This one breaks our editors constantly. Not even assigned.

I could go on, but I feel that I’ve made my point. Bugs, even major ones are either closed as “wontfix”, or are ignored for years despite the pain felt by users. That’s not even covering the dismissive and distrustful attitude given in some of the replies.

Strange Priorities

The Clojure team appears to be super focused on new features, at the exclusion of existing namespaces. The big highlights from the past year or so have been Transit, Transducers, and Spec.

These are okay, I guess. We use transit a bit, and it’s kinda cool. But we really don’t use 90% of its features, it’s basically JSON for us that can convert numbers to BigDecimals.

We have yet to find a place that Transducers would help us. They’re neat enough, but the built in lazy sequences are working A-OK for us, so we don’t really feel the need to change over.

I’m not holding my breath for Spec. It doesn’t fix anything for me that other libraries aren’t already providing.

Know what hasn’t seen any major improvements in forever? Clojure.test. Clojure.Test is frankly sad. Fixtures are done via some global state, and you can’t even setup fixtures to work across the entire test suite. Need a database to run functional tests? Well either you need to override the main test runner (good luck running individual tests now!) or you have to setup each namespace to open and close its own database connection (don’t forget, or your DBA will wonder why Emacs has 1000+ database connections). I’m 100% behind the idea that I’ll have to write a bit of glue code, but without anywhere to put that code I’m kind of screwed.

And then there is the is function. It’s literally the only assertion provided by clojure.test. It’s this fancy little macro that grabs its body, evaluates it, then uses the body to produce a human readable message about the failure.

And it’s garbage. The fact that plugins exist to make this easier on the eyes should tell you everything you need to know. Oh but don’t use that with Emacs/Cider! It’ll crash the Cider plugin, which is trying to parse the default output.

Back when I used Emacs, I had a stash on my box that disabled AOT, pedantic checking, and the humane-test-output plugin from my project.clj in order to use Cider. Without that stash applied Cider wouldn’t start, couldn’t reload code, and would crash when running tests. Now that I use Cursive that’s less of an issue, but it’s still kind of nuts I had to decide between a working editor and readable output when I ran lein test

Sorry, I didn’t even highlight the craziest bit of that last paragraph, did you catch it? I had to disable humane-test-output from my project.clj. That’s because you install it by injecting some code in project.clj that redefines some multi-methods, because there’s no plugin architecture. How nuts is that?

Now I might hear you say “You don’t have to use clojure.test!”, and you’re right. But clojure.test has clearly won in the Clojure testing namespace. The only real competitors for clojure.test are Speclj and Midje. I’ve literally never met someone in person that’s used Speclj, and Midje is super polarizing because it’s basically a collection of magic macros. The fact that the second entry for Midje is about CircleCI rewriting from Midje to clojure.test should tell you a lot.

So why don’t we have more creature comforts for clojure.test? I’m not really sure. As far as I can tell the change to it was the inclusion of test.check, but that really was nothing more than simple-check getting renamed and transferred to Clojure ownership.

Okay, Now What?

As I stated before, this isn’t a “I’m quitting Clojure!” post. Partly this is because I work in Clojure on a daily basis, and I both like my job and am professional enough to keep working despite my complaints. And partly this is because I do not have a replacement for Clojure in mind for my own personal projects. But off the top of my head, there are the things I’d like to see fixed in the Clojure areas.

More love for clojure.test.

No tolerance for bugs that result in bad-data. Built in functions should either work, or throw an understandable exception.

Friendlier responses in Jira. Someone who has gone to the work to sign up and try to help out should be treated with more respect.

Fix underlying compiler bugs before adding features. The other way only codifies bad behavior and guarantees that it cannot be fixed.

Understand that if enough people have the same issue, it’s the codes fault and a FAQ entry is not good enough.

Basically I want Clojure to be a simple to use language backed by a friendly and active community. What I see now is drifting in the wrong direction, and I’d like to see that corrected.

Introduction

I was recently on
The Cognicast
with Craig Andera where we discussed using Generative Testing on a large
non-Clojure(script) codebase, in particular Ruby on Rails and
Backbonejs. If you haven’t listened to the show yet I highly recommend
it first.

As I promised on the show, I’d like to share how we used Test.Check to
test our Backbone.js code base. Our overall strategy for testing
Javascript is going to be:

Compile JS into one file (just like prod).

Compile tests into a single file.

Combine them in a PhantomJS process.

Let the tests do their thing.

While we have been super pleased with the results of Generative Testing,
there have been some hurdles for getting it to work for us. In this post
I’m going to go over how to setup Test.Check to work with your
Javascript app, and how to dodge all the pitfalls I found.

Here are the challenges that lie between us and Generative Testing bliss.

Picking the right library

Setting up Leiningen & Cljsbuild

Dodge PhantomJS issues

Avoid mangling your app, and defeating dueling dependencies

Picking the Right Library

First of all, there are two libraries that exist,
Test.Check and
DoubleCheck. Because
Test.Check is an official Clojure library it is Clojure (JVM) only, so I
recommend DoubleCheck (maintained by Chas Emerick) which is capable of
cross compiling to Clojure and Clojurescript.

The only catch with DoubleCheck is that it’s not currently possible to
segregate tests via metadata for running in groups. But with the way we
will be running these tests that won’t be an issue.

Setting up Leiningen

First step, install Leiningen and create a
project.clj wherever you Javascript code is. We’re going to use
Cljsbuild to compile our
testing code for execution. I in put my test code in test/cljs (because
I have clj and cljs based tests), and send the compiled output to
tmp/tracker-cljs.js. Note: this guide only works for Clojurescript
0.0-2234, I need to figure out why the latest build of Clojurescript
doesn’t work.

I highly recommend you send the output of the compilation process to
either a temporary or gitignored location. The output will be fairly
large, and it will bog down your repository with its size.

I don’t want to duplicate the Cljsbuild how-to, so if you don’t know how
to make it work, you should check their docs. Our project.clj is
reproduced at the bottom of this post if you have issues.

That means that your app code is trying to access local storage, and
PhantomJS does not like it when you do that without loading a
webpage. The solution for this is to start a server so we have a page to
visit, and visit it via PhantomJS. So on Tracker we use the following
two bits of code.

And a rake task to stand up a server and run everything. If you don’t
use Rails for your Javascript code you might need to use different
commands to compile, but the intention remains. The WEBrick server
provides a blank page for us to visit and run our tests on, which
prevents PhantomJS from raising security errors.

To make all this work together, update project.clj to reference the
generative_runner.js file instead of :runner, and use rake
test:generative to kick off the run.

Don’t Mangle Your Code

If your application is anything like Tracker, you might use some Google
Closure dependencies without using the entire Closure compiler. And even
if you don’t need use Closure, you certainly have functions and classes
in the global namespace that you don’t want mangled.

To get around this, I recommend the following settings:

Add :libs [ "compiled-application.js" ""] to the cljsbuild section
in project.clj. This prevents DoubleCheck compiler errors due to
classpath issues, and it allows the Closure compiler to see everything
that your application provides. So if your tests and applications have
overlapping Closure dependencies you won’t get double provide errors.

Secondly I recommend that you only use the simple compilation mode. This
will prevent Closure from mangling global names, which will make
debugging easier and prevent your tests from being able to find the
production code. The space saving and code elimination that advanced
mode provides is more of a problem than a benefit for testing, so it’s
not worth fighting to get advanced to work.

You can fiddle with source maps if you wish, but I
haven’t had much luck or use for them; simple compiled Clojurescript is
easy to read, and most of the serious errors have come from the 43k
application javascript file, not the test file.

Have Fun and Make More Tests

Once you have that going, it should be possible to open up and create
increasingly complicated tests. As a teaser and a good example, the
following code caught a tricky JS ordering bug.

The internet is lousy with productivity ideas, mostly about how to work harder or longer. I personally believe that good productivity is about maximizing per hour results, not working harder. And the fastest way to improve your productivity is to eliminate some of the things slowing you down. So rather than going over the usual suspects, let’s take a look at eliminating some of the low hanging fruit.

1. Conquer Your Diet.

What you eat is the cornerstone of who you are and what you do. The proteins in your muscle, the fats in your cell walls and your brain, and the amino acids used throughout your body must all come from, or be synthesized from your food. Low quality food products like trans fats have been connected with apathy, depression, and might be related to ADD. In order for your brain and body to perform at peak levels you need to give it high quality food to repair and refuel.

2. Sleep

Sleep is massively underrated. Everyone knows at this point that Americans are typically not getting enough sleep on a nightly basis, but distressingly few people consider it to be an issue. After all, hours spent asleep aren’t hours spent working right? Unfortunately it isn’t that simple. Insufficient or low quality sleep is one of the fastest ways that we know of to destroy per hour productivity. Get sleep or waste your precious waking hours in a mind fog.

3. Know when to stop.

Athletes regularly destroy their bodies by “over training”; exercising to the point of injury or chronic fatigue. The results of over training are often slow growth or even frustrating setbacks despite hundreds of hours at the gym or on the track.

Mental workers regularly do the exact same thing to their mind by working well past the point of mental burnout. There is very little use in working when you are not going to be putting out at least average results, and you risk ruining your morale after hours of low-results work. Instead of ritualistically working even when you are spent gain an intuitive sense for when you will accomplish little and instead go recharge and try again later.

4. You must come out unharmed 1 time or 1000 times.

No productivity regime is worthwhile if you can only maintain it for 2 weeks at the start of each year. To be successful, you must find a pattern that you can maintain years. Whether that is a small amount per day or a cycle between intense work and relaxation, you must find a balance between work and play or you will be plagued by failures to meet your goals.

5. Know thyself.

Everyone wants to be successful, but most people overestimate how much money motivates them. Find ways to motivate yourself with something other than riches, or better yet find something that you love to do. Love of the work will make it easier to get up, or home, every day and work rather than abstract future financial rewards.

One of the largest mistakes you can make as a knowledge worker is to focus 100% of your time on your craft. It’s easy to believe that specializing and focusing will make you better than your peers, but I do not think that is the case. Not only will specializing cause you to plateau earlier than your peers, it will cause you to be less happy and healthy than your diversified peers.

A bit of background at this point would probably be helpful. I’m a software engineer and I’ve coded both professionally and on a hobbyist basis for nearly a decade. After a particularly stressful few months at a previous job, my fiancé forced me to join a local gym to de-stress. The gym focused on various European martial arts and I ended up in a class for the Italian longsword circa 1409.

A few years ago I would have suspected that choosing to surrender 2-8 hours a week to swinging steel around instead of programming on hobbyist projects would slow down my growth. Now I believe that I owe a lot of my growth, professionally and as a human being to this practice. A lot of what I’m going to cover here will probably be old news to anyone who was heavily involved in sports, nor is it particularly unique to fencing. That being said I’m fairly confident that a lot of knowledge workers have lost the involvement with physical activities they might have had, or never were all that “athletic” even in school.

The biggest modern challenge is that humans are not wired for the type of work that we now do. We are a fairly clever species by nature, which is why we have been making art for hundreds of thousands of years. But we are still more or less genetically and mentally hunter gatherers from 100,000+ years ago who largely worked for their survival. Our bodies, genes, and minds are wired to expect a specific ratio of play and physical activity to signal that all is well. Unfortunately we work a lot longer than our ancestors did, and under very different conditions.

This high level of nothing but work, physical or mental, indicates to our bodies that times are tough and that it should release stress hormones to help us survive the coming hard times; these stress hormones tend to have serious detrimental effects on mental performance and long term health. The phrase “all work and no play” may be over-used, but it does have some truth. All work and no play leaves Jack pumped full of cortisol, short on sleep, and low on testosterone.

Secondly, most knowledge workers spend their entire time thinking only with their frontal cortex, or the analytical portion of the brain. This is very helpful if your job involves concentrating on difficult problems all day, but it is incredibly easy to let that portion of your brain become the only driving factor on your day to day life. The brain, like your muscles, should have ample opportunity to exercise all of its faculties and have recovery time between each heavy usage. Expecting it to be able to focus deeply on your work day in and day out without giving it time to relax is simply asking for lowered performance and burnout, and only working on one aspect of your mental performance is akin to only doing curls at the gym; the result is an odd shape with very little practical strength.

Thankfully exercise and hobbies help other parts of the brain. This is one of the things I love about fencing: it isn’t very analytical once you actually start using it. There are a ton of cuts and guards to be memorized, but you never have time to think about it when you are actually fencing. All of the drills are designed so that your mind and body learn to move with instinctual grace from one guard to another. There is very little conscious thought that happens mid-move in a match; there simply is not enough time to stop and think. Instead you learn to have an internalized notion of time and measure, and an ability to make new decisions as the fight progresses quickly and correctly.

And while none of this directly relates to software engineering, it has a positive effect on my daily work. The ability to move with a fight and think with my toes and fingertips have given me a greater appreciation to the importance of gut instinct in more situations. And the constant practice of excluding my analytical mind in a fast moving match have improved my ability to enter a flow state more easily. Combine these things with the general good effects of mental down time and exercise, and I think it’s hard to argue that my time would have been better spent working on hobbyist projects.

I am a language snob. In particular I fall head over heels for most functional languages, especially MLs and Lisps. Show me the latest and greatest Javascript framework and I will just wish I had immutable data types and a saner method dispatch system. I try to keep quiet about it at work with varying degrees of success, but it’s frustrating to work around one language’s problems when you know of other solutions.

The difficult reality that few admit is that every language has a weak point. Ruby is slow and the lack of import semantics and proper namespaces makes it difficult to determine what code will run. Python lacks a good lambda and whitespace sensitivity brings new difficulties. Javascript is just pure insanity. Lisp is poorly standardized and tends to have subpar documentation. The list goes on and on. No language is perfect, but it’s very easy to focus on the high points of one language while working through the low points of another.

The real bummer about being a language snob is that there’s really nothing to be done about it. For any given issue there will always be another language that exceeds in that area, but it’s almost always insane and impractical to convert your entire company over to it. So even if you think Go would solve every problem that your Python codebase has, and you know that the downsides wouldn’t be insurmountable, the simple reality is that convincing the organization to throw away their perfectly good Python code on a whim is insane at best. And that’s assuming that converting to your language wouldn’t come with downsides worse than the language you are coming from.

Thankfully, there is an upside to being a language snob. Polyglots have a far more flexible understanding of what a program should do, especially when they’re used to a wide range of paradigms. Someone who has done nothing but C or Java programming might have very little context into why mutable state can be so problematic, but someone who knows Clojure or Haskell will know the tradeoffs of mutable vs. immutable state intimately. Each new paradigm a programmer embraces means that they have more internal views on how a particular problem could be solved, a bit like having an experienced team in your head to discuss the merits of various techniques at lightning speed.

While it may be very frustrating to know of better solutions in other languages, there is a benefit to knowing about them. Being a language snob can help you evaluate your choices more effectively and discover solutions and strategies that might not be immediately obvious if you only knew one language. And between being a bit of a snob and attempting to shoe-horn every problem into a one-size fits all paradigm, I’ll take being a snob.

No programming language stands alone. Besides the compiler, every
programming language includes an ecosystem of libraries, build tools,
analyzers, debuggers, and other utilities. Languages often rise and fall
depending on the quality of these tools and libraries.

For every language there needs to be one central item upon which every
other tool depends. In most languages, this is the compiler or
interpreter. Your Rails project is entirely dependent on the version of
Ruby provided by the current environment, and similarly Maven depends on
the version of javac and java available on the path.

Unfortunately, this makes our code more fragile and dependent on the
machine it was first created on. Someone cloning your code from a
different machine must take care to ensure that their development
environment is close to the original authors, and deployment must ship
the correct compilers and interpeters for production to work well. We
have created tools to help enforce the requirements of the code, but
they are fragile and make upgrading dependencies a pain, as anyone who
has had to fight with RVM can attest.

The one exception to this I have found is Clojure. Clojure inverts the
normal order making the build tool the central item, with the compiler
provided by the project definition file.

The beauty of this change is that it makes setup trivial for another
developer. All they need is the same build tool, and it will deal with
the correct versions of both the compiler and any libraries for the
project. Have other projects that depend on different versions of the
compiler? The build tool only cares about the dependencies in front of
it, and will call the correct version from the correct project.

This also makes upgrading trivial. Want to try Clojure 1.6.0? Change
“1.5.1” to “1.6.0” in the above snippet. Want to write a library that
supports multiple versions of the compiler? The build tool supports
profiles which allow you to swap out compilers trivially because it’s
just a dependency.

Deployment gets easier as well. If you’re deploying an uberjar, the core
libraries you tested against are also shipped to production in the same
jar. No need to upgrade your deployment scripts when a new version of
Clojure comes out, as everything is included automatically.

There is one catch to this wonderfulness, which is that Clojure depends
on the JVM, and the build tool cannot change the JVM around. But Clojure
has very simple requirements, Java 1.6 or greater, which
makes it simple to deploy anywhere.

One of my hobbies is fencing. Not modern Olympic fencing, but 14th
century longsword fencing in the Italian school. In every class the
instructor reminds us that we should act “like haughty Italian nobles,
tall and relaxed” in the way that we stand, move, and handle the weapon.

The word “Sprezzatura” crops up a lot in these discussions. The simplest
translation is literally “disdain”, but a more careful translation would
be “studied carelessness”. To act with Sprezzatura means to make learned
actions look easy and natural.

It turns out there is a really good reason for a fencer to act this
way. A relaxed and calm fencer can move more rapidly and adjust their
actions depending on whether they are winning or losing the bind. Their
actions happen without any tell, catching their opponent by surprise and
allowing them to act within their opponents tempo. In sword fighting the
ultimate goal is to strike your opponent without being hit. Thus the
shortest and simplest actions are favored, as large embellished actions
only increase personal risk.

There is a similar grace in programming. While programming is not
usually a competitive or dangerous hobby, there are practical benefits
to acting quickly and gracefully. The most effective engineer completes
their task with the minimal amount of time and added complexity. Showing
off in the code only increases the risk of regressions and makes the
code harder to modify later. The ultimate goal is a maintainable
application that meets requirements in the minimal amount of time, and
an accomplished engineer will take the shortest route to that goal.

Sword fighting is not about strength. It is never effective to swing a
sword with all of your strength. Even if this wasn’t unsafe and an
obvious tell, it isn’t a good way to cut with an edged weapon. Swords
depend on a cutting edge to do damage, and thus a smooth arc that draws
the weapon through the target will always cut more effectively than a
ham-fisted baseball swing.

Similarly, the accomplished engineer knows that completing a task is not
about the number of hours spent, but the quality. The mind is a tool
that can be both sharpened and dulled with both work and rest, and
programming is a task of the mind. Thus the quality engineer avoids
excessive hours, as they are unhealthy and ineffective. Instead the
engineer makes their limited productive hours as effective as possible
without excessive strain.

It may not be the connection you expected, but humans haven’t changed in
a very long time. Even if our circumstances have changed, there is
always something to be learned from even the most esoteric sources.

I’m getting a little tired of seeing posts saying that the best managers
must be an ex-engineer or a current one. I think coding skill is a very
narrow minded way to judge both a human and a professional, and a
terrible way to run a business.

Here’s the simple truth, a manager is a craftsperson just like a
designer or engineer. The only difference is that their craft is
organizing people, not designs or code. In their trade the best tools
are flexibility, communication, empathy, and comprehension. My personal
opinion is that a good manager is at least as hard to find as a good
engineer, if not harder.

To say that a manager of engineers needs to know how code and needs to
code daily is like saying that you have to be a doctor to manage a
doctor’s office, and that you need to be practicing right now. While a
bit of knowledge aids in communication, there are a wide range of tasks
that need to be performed that are not coding. And to ignore these
tasks would be just as disastrous as not writing the software that the
company sells.

We’ve all heard the horror stories of non-engineer managers not knowing
or caring what engineers do, or the ex-engineer manager thinking that
they’re still on the team when their knowledge is 20 years out of
date. Clearly both of these people are being ineffective managers, but
not because of whether or not they can code. These people are being bad
managers because they are not listening to their staff. Anyone, coder
or not, will be a terrible manager without the requisite people skills.

So hire and keep managers that listen and communicate well. Managers
that manage expectations of those uphill and divert shit rolling
downhill. The best manager is the one that helps their team be the best,
whether or not there are any commits with their name on it.